#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@ProjectName: Homalos_v2
@FileName   : strategy_health_monitor.py
@Date       : 2025/7/15 10:00
@Author     : Donny
@Email      : donnymoving@gmail.com
@Software   : PyCharm
@Description: 策略健康监控器 - 监控策略运行状态、异常检测和自动恢复
"""
import asyncio
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
from typing import Dict, List, Optional, Callable, Any

from src.config.config_manager import ConfigManager
from src.core.event import Event, EventType
from src.core.event_bus import EventBus
from src.core.logger import get_logger
from src.strategy.base_strategy import BaseStrategy


class HealthStatus(Enum):
    """健康状态枚举"""
    HEALTHY = "healthy"
    WARNING = "warning"
    CRITICAL = "critical"
    UNKNOWN = "unknown"


class AnomalyType(Enum):
    """异常类型枚举"""
    PERFORMANCE_DEGRADATION = "performance_degradation"
    MEMORY_LEAK = "memory_leak"
    HIGH_ERROR_RATE = "high_error_rate"
    STUCK_EXECUTION = "stuck_execution"
    RESOURCE_EXHAUSTION = "resource_exhaustion"
    NETWORK_ISSUES = "network_issues"
    DATA_QUALITY_ISSUES = "data_quality_issues"
    POSITION_DRIFT = "position_drift"
    RISK_LIMIT_BREACH = "risk_limit_breach"


@dataclass
class HealthMetric:
    """健康指标"""
    name: str
    value: float
    threshold: float
    unit: str
    timestamp: datetime = field(default_factory=datetime.now)
    is_critical: bool = False


@dataclass
class Anomaly:
    """异常信息"""
    type: AnomalyType
    severity: HealthStatus
    message: str
    details: Dict[str, Any]
    timestamp: datetime = field(default_factory=datetime.now)
    strategy_id: str = ""
    resolved: bool = False
    resolution_time: Optional[datetime] = None


@dataclass
class HealthReport:
    """健康报告"""
    strategy_id: str
    status: HealthStatus
    metrics: List[HealthMetric]
    anomalies: List[Anomaly]
    last_check_time: datetime
    uptime: timedelta
    performance_score: float = 0.0
    recommendations: List[str] = field(default_factory=list)


class StrategyHealthMonitor:
    """策略健康监控器"""
    
    def __init__(self, event_bus: EventBus, config: Optional[ConfigManager] = None):
        self.event_bus = event_bus
        self.config = config
        self.logger = get_logger(self.__class__.__name__)
        
        # 监控配置
        self.check_interval = 30  # 检查间隔（秒）
        self.metric_history_size = 100  # 指标历史记录大小
        self.anomaly_threshold = 3  # 异常阈值
        self.auto_recovery_enabled = True
        
        # 监控状态
        self.is_monitoring = False
        self.monitored_strategies: Dict[str, BaseStrategy] = {}
        self.strategy_start_times: Dict[str, datetime] = {}
        
        # 健康数据
        self.health_reports: Dict[str, HealthReport] = {}
        self.metric_history: Dict[str, List[HealthMetric]] = {}
        self.anomaly_history: Dict[str, List[Anomaly]] = {}
        
        # 恢复处理器
        self.recovery_handlers: Dict[AnomalyType, Callable] = {}
        
        # 监控任务
        self.monitor_task: Optional[asyncio.Task] = None
        
        # 加载配置
        self._load_config()
        
        # 注册默认恢复处理器
        self._register_default_recovery_handlers()
    
    def _load_config(self):
        """加载配置"""
        if not self.config:
            return
        
        try:
            health_config = self.config.get("strategy_management.health_monitor", {})
            self.check_interval = health_config.get("check_interval", 30)
            self.metric_history_size = health_config.get("metric_history_size", 100)
            self.anomaly_threshold = health_config.get("anomaly_threshold", 3)
            self.auto_recovery_enabled = health_config.get("auto_recovery_enabled", True)
            
            self.logger.info(f"健康监控配置加载完成: 检查间隔={self.check_interval}s")
        except Exception as e:
            self.logger.error(f"加载健康监控配置失败: {e}")
    
    def _register_default_recovery_handlers(self):
        """注册默认恢复处理器"""
        self.recovery_handlers = {
            AnomalyType.HIGH_ERROR_RATE: self._handle_high_error_rate,
            AnomalyType.STUCK_EXECUTION: self._handle_stuck_execution,
            AnomalyType.MEMORY_LEAK: self._handle_memory_leak,
            AnomalyType.PERFORMANCE_DEGRADATION: self._handle_performance_degradation,
            AnomalyType.RESOURCE_EXHAUSTION: self._handle_resource_exhaustion,
        }
    
    async def start_monitoring(self) -> None:
        """开始监控"""
        if self.is_monitoring:
            self.logger.warning("健康监控已在运行")
            return
        
        self.is_monitoring = True
        self.monitor_task = asyncio.create_task(self._monitor_loop())
        self.logger.info("策略健康监控已启动")
    
    async def stop_monitoring(self) -> None:
        """停止监控"""
        if not self.is_monitoring:
            return
        
        self.is_monitoring = False
        if self.monitor_task:
            self.monitor_task.cancel()
            try:
                await self.monitor_task
            except asyncio.CancelledError:
                pass
        
        self.logger.info("策略健康监控已停止")
    
    def add_strategy(self, strategy: BaseStrategy) -> None:
        """添加策略到监控"""
        strategy_id = strategy.strategy_id
        self.monitored_strategies[strategy_id] = strategy
        self.strategy_start_times[strategy_id] = datetime.now()
        self.metric_history[strategy_id] = []
        self.anomaly_history[strategy_id] = []
        
        # 初始化健康报告
        self.health_reports[strategy_id] = HealthReport(
            strategy_id=strategy_id,
            status=HealthStatus.HEALTHY,
            metrics=[],
            anomalies=[],
            last_check_time=datetime.now(),
            uptime=timedelta(0)
        )
        
        self.logger.info(f"策略 {strategy_id} 已添加到健康监控")
    
    def remove_strategy(self, strategy_id: str) -> None:
        """从监控中移除策略"""
        self.monitored_strategies.pop(strategy_id, None)
        self.strategy_start_times.pop(strategy_id, None)
        self.health_reports.pop(strategy_id, None)
        self.metric_history.pop(strategy_id, None)
        self.anomaly_history.pop(strategy_id, None)
        
        self.logger.info(f"策略 {strategy_id} 已从健康监控中移除")
    
    async def check_strategy_health(self, strategy_id: str) -> HealthReport:
        """检查单个策略健康状态"""
        if strategy_id not in self.monitored_strategies:
            raise ValueError(f"策略 {strategy_id} 未在监控中")
        
        strategy = self.monitored_strategies[strategy_id]
        start_time = self.strategy_start_times[strategy_id]
        
        # 收集健康指标
        metrics = await self._collect_health_metrics(strategy)
        
        # 检测异常
        anomalies = await self._detect_anomalies(strategy_id, metrics)
        
        # 计算健康状态
        status = self._calculate_health_status(metrics, anomalies)
        
        # 计算性能分数
        performance_score = self._calculate_performance_score(metrics)
        
        # 生成建议
        recommendations = self._generate_recommendations(metrics, anomalies)
        
        # 更新健康报告
        health_report = HealthReport(
            strategy_id=strategy_id,
            status=status,
            metrics=metrics,
            anomalies=anomalies,
            last_check_time=datetime.now(),
            uptime=datetime.now() - start_time,
            performance_score=performance_score,
            recommendations=recommendations
        )
        
        self.health_reports[strategy_id] = health_report
        
        # 更新历史记录
        self._update_metric_history(strategy_id, metrics)
        self._update_anomaly_history(strategy_id, anomalies)
        
        # 发布健康检查事件
        self._publish_health_event(health_report)
        
        return health_report
    
    async def detect_anomalies(self, strategy_id: str) -> List[Anomaly]:
        """检测策略异常"""
        if strategy_id not in self.monitored_strategies:
            return []
        
        strategy = self.monitored_strategies[strategy_id]
        metrics = await self._collect_health_metrics(strategy)
        return await self._detect_anomalies(strategy_id, metrics)
    
    async def attempt_recovery(self, strategy_id: str, anomaly: Anomaly) -> bool:
        """尝试恢复策略"""
        if not self.auto_recovery_enabled:
            self.logger.info(f"自动恢复已禁用，跳过策略 {strategy_id} 的恢复")
            return False
        
        if anomaly.type not in self.recovery_handlers:
            self.logger.warning(f"未找到异常类型 {anomaly.type} 的恢复处理器")
            return False
        
        try:
            # 发布恢复开始事件
            self.event_bus.publish(Event(
                EventType.STRATEGY_RECOVERY_STARTED,
                data={
                    "strategy_id": strategy_id,
                    "anomaly_type": anomaly.type.value,
                    "anomaly_message": anomaly.message
                }
            ))
            
            # 执行恢复
            handler = self.recovery_handlers[anomaly.type]
            success = await handler(strategy_id, anomaly)
            
            if success:
                anomaly.resolved = True
                anomaly.resolution_time = datetime.now()
                
                # 发布恢复完成事件
                self.event_bus.publish(Event(
                    EventType.STRATEGY_RECOVERY_COMPLETED,
                    data={
                        "strategy_id": strategy_id,
                        "anomaly_type": anomaly.type.value,
                        "recovery_time": anomaly.resolution_time.isoformat()
                    }
                ))
                
                self.logger.info(f"策略 {strategy_id} 异常 {anomaly.type.value} 恢复成功")
            else:
                # 发布恢复失败事件
                self.event_bus.publish(Event(
                    EventType.STRATEGY_RECOVERY_FAILED,
                    data={
                        "strategy_id": strategy_id,
                        "anomaly_type": anomaly.type.value,
                        "reason": "恢复处理器执行失败"
                    }
                ))
                
                self.logger.error(f"策略 {strategy_id} 异常 {anomaly.type.value} 恢复失败")
            
            return success
            
        except Exception as e:
            self.logger.error(f"策略 {strategy_id} 恢复过程异常: {e}")
            
            # 发布恢复失败事件
            self.event_bus.publish(Event(
                EventType.STRATEGY_RECOVERY_FAILED,
                data={
                    "strategy_id": strategy_id,
                    "anomaly_type": anomaly.type.value,
                    "reason": str(e)
                }
            ))
            
            return False
    
    def get_health_report(self, strategy_id: str) -> Optional[HealthReport]:
        """获取健康报告"""
        return self.health_reports.get(strategy_id)
    
    def get_all_health_reports(self) -> Dict[str, HealthReport]:
        """获取所有健康报告"""
        return self.health_reports.copy()
    
    def get_metric_history(self, strategy_id: str, metric_name: str = None) -> List[HealthMetric]:
        """获取指标历史"""
        history = self.metric_history.get(strategy_id, [])
        if metric_name:
            return [m for m in history if m.name == metric_name]
        return history
    
    def get_anomaly_history(self, strategy_id: str) -> List[Anomaly]:
        """获取异常历史"""
        return self.anomaly_history.get(strategy_id, [])
    
    def register_recovery_handler(self, anomaly_type: AnomalyType, handler: Callable) -> None:
        """注册恢复处理器"""
        self.recovery_handlers[anomaly_type] = handler
        self.logger.info(f"已注册异常类型 {anomaly_type.value} 的恢复处理器")
    
    async def _monitor_loop(self):
        """监控循环"""
        while self.is_monitoring:
            try:
                # 检查所有策略健康状态
                for strategy_id in list(self.monitored_strategies.keys()):
                    try:
                        await self.check_strategy_health(strategy_id)
                    except Exception as e:
                        self.logger.error(f"检查策略 {strategy_id} 健康状态失败: {e}")
                
                # 等待下次检查
                await asyncio.sleep(self.check_interval)
                
            except asyncio.CancelledError:
                break
            except Exception as e:
                self.logger.error(f"健康监控循环异常: {e}")
                await asyncio.sleep(5)  # 短暂等待后继续
    
    async def _collect_health_metrics(self, strategy: BaseStrategy) -> List[HealthMetric]:
        """收集健康指标"""
        metrics = []
        
        try:
            # CPU使用率
            cpu_usage = self._get_cpu_usage(strategy)
            metrics.append(HealthMetric(
                name="cpu_usage",
                value=cpu_usage,
                threshold=80.0,
                unit="%",
                is_critical=cpu_usage > 90.0
            ))
            
            # 内存使用率
            memory_usage = self._get_memory_usage(strategy)
            metrics.append(HealthMetric(
                name="memory_usage",
                value=memory_usage,
                threshold=85.0,
                unit="%",
                is_critical=memory_usage > 95.0
            ))
            
            # 错误率
            error_rate = self._get_error_rate(strategy)
            metrics.append(HealthMetric(
                name="error_rate",
                value=error_rate,
                threshold=5.0,
                unit="%",
                is_critical=error_rate > 10.0
            ))
            
            # 响应时间
            response_time = self._get_response_time(strategy)
            metrics.append(HealthMetric(
                name="response_time",
                value=response_time,
                threshold=1000.0,
                unit="ms",
                is_critical=response_time > 5000.0
            ))
            
            # 订单成功率
            order_success_rate = self._get_order_success_rate(strategy)
            metrics.append(HealthMetric(
                name="order_success_rate",
                value=order_success_rate,
                threshold=95.0,
                unit="%",
                is_critical=order_success_rate < 90.0
            ))
            
        except Exception as e:
            self.logger.error(f"收集健康指标失败: {e}")
        
        return metrics
    
    async def _detect_anomalies(self, strategy_id: str, metrics: List[HealthMetric]) -> List[Anomaly]:
        """检测异常"""
        anomalies = []
        
        try:
            # 检查指标阈值
            for metric in metrics:
                if metric.is_critical:
                    anomaly = Anomaly(
                        type=self._map_metric_to_anomaly_type(metric.name),
                        severity=HealthStatus.CRITICAL,
                        message=f"{metric.name} 达到临界值: {metric.value}{metric.unit}",
                        details={
                            "metric_name": metric.name,
                            "current_value": metric.value,
                            "threshold": metric.threshold,
                            "unit": metric.unit
                        },
                        strategy_id=strategy_id
                    )
                    anomalies.append(anomaly)
                elif metric.value > metric.threshold:
                    anomaly = Anomaly(
                        type=self._map_metric_to_anomaly_type(metric.name),
                        severity=HealthStatus.WARNING,
                        message=f"{metric.name} 超过阈值: {metric.value}{metric.unit}",
                        details={
                            "metric_name": metric.name,
                            "current_value": metric.value,
                            "threshold": metric.threshold,
                            "unit": metric.unit
                        },
                        strategy_id=strategy_id
                    )
                    anomalies.append(anomaly)
            
            # 检查趋势异常
            trend_anomalies = self._detect_trend_anomalies(strategy_id, metrics)
            anomalies.extend(trend_anomalies)
            
        except Exception as e:
            self.logger.error(f"检测异常失败: {e}")
        
        return anomalies

    @staticmethod
    def _calculate_health_status(metrics: List[HealthMetric], anomalies: List[Anomaly]) -> HealthStatus:
        """计算健康状态"""
        if any(a.severity == HealthStatus.CRITICAL for a in anomalies):
            return HealthStatus.CRITICAL
        elif any(a.severity == HealthStatus.WARNING for a in anomalies):
            return HealthStatus.WARNING
        elif any(m.is_critical for m in metrics):
            return HealthStatus.CRITICAL
        elif any(m.value > m.threshold for m in metrics):
            return HealthStatus.WARNING
        else:
            return HealthStatus.HEALTHY

    @staticmethod
    def _calculate_performance_score(metrics: List[HealthMetric]) -> float:
        """计算性能分数，基于各项指标与阈值的比例关系"""
        if not metrics:
            return 0.0
        
        total_score = 0.0

        for metric in metrics:
            if metric.threshold > 0 and metric.value is not None:
                # 计算指标得分（0-100），比例限制在0-2之间
                ratio = min(metric.value / metric.threshold, 2.0)  # 最大比例为2
                metric_score = max(0, 100 - (ratio - 1) * 100)  # 超过阈值开始线性扣分
                total_score += metric_score

        return total_score / len(metrics)

    @staticmethod
    def _generate_recommendations(metrics: List[HealthMetric], anomalies: List[Anomaly]) -> List[str]:
        """生成建议"""
        recommendations = []
        
        # 基于指标的建议
        for metric in metrics:
            if metric.value > metric.threshold:
                if metric.name == "cpu_usage":
                    recommendations.append("考虑优化算法复杂度或增加计算资源")
                elif metric.name == "memory_usage":
                    recommendations.append("检查内存泄漏或优化数据结构")
                elif metric.name == "error_rate":
                    recommendations.append("检查错误日志并修复相关问题")
                elif metric.name == "response_time":
                    recommendations.append("优化网络连接或减少数据处理延迟")
                elif metric.name == "order_success_rate":
                    recommendations.append("检查交易网关连接和订单参数")
        
        # 基于异常的建议
        for anomaly in anomalies:
            if anomaly.type == AnomalyType.PERFORMANCE_DEGRADATION:
                recommendations.append("监控系统资源使用情况并考虑扩容")
            elif anomaly.type == AnomalyType.HIGH_ERROR_RATE:
                recommendations.append("分析错误模式并实施错误处理改进")
            elif anomaly.type == AnomalyType.MEMORY_LEAK:
                recommendations.append("进行内存分析并修复内存泄漏")
        
        return list(set(recommendations))  # 去重
    
    def _update_metric_history(self, strategy_id: str, metrics: List[HealthMetric]):
        """更新指标历史"""
        if strategy_id not in self.metric_history:
            self.metric_history[strategy_id] = []
        
        history = self.metric_history[strategy_id]
        history.extend(metrics)
        
        # 保持历史记录大小
        if len(history) > self.metric_history_size:
            self.metric_history[strategy_id] = history[-self.metric_history_size:]
    
    def _update_anomaly_history(self, strategy_id: str, anomalies: List[Anomaly]):
        """更新异常历史"""
        if strategy_id not in self.anomaly_history:
            self.anomaly_history[strategy_id] = []
        
        self.anomaly_history[strategy_id].extend(anomalies)
        
        # 发布异常检测事件
        for anomaly in anomalies:
            try:
                self.event_bus.publish(Event(
                    EventType.STRATEGY_ANOMALY_DETECTED,
                    data={
                        "strategy_id": strategy_id,
                        "anomaly_type": anomaly.type.value,
                        "severity": anomaly.severity.value,
                        "message": anomaly.message,
                        "details": anomaly.details
                    }
                ))
            except Exception as e:
                self.logger.error(f"发布异常检测事件失败: {e}")
    
    def _publish_health_event(self, health_report: HealthReport):
        """发布健康检查事件"""
        self.event_bus.publish(Event(
            EventType.STRATEGY_HEALTH_CHECK,
            data={
                "strategy_id": health_report.strategy_id,
                "status": health_report.status.value,
                "performance_score": health_report.performance_score,
                "metrics_count": len(health_report.metrics),
                "anomalies_count": len(health_report.anomalies),
                "uptime_seconds": health_report.uptime.total_seconds(),
                "check_time": health_report.last_check_time.isoformat()
            }
        ))
    
    def _map_metric_to_anomaly_type(self, metric_name: str) -> AnomalyType:
        """将指标名称映射到异常类型"""
        mapping = {
            "cpu_usage": AnomalyType.PERFORMANCE_DEGRADATION,
            "memory_usage": AnomalyType.MEMORY_LEAK,
            "error_rate": AnomalyType.HIGH_ERROR_RATE,
            "response_time": AnomalyType.PERFORMANCE_DEGRADATION,
            "order_success_rate": AnomalyType.NETWORK_ISSUES
        }
        return mapping.get(metric_name, AnomalyType.PERFORMANCE_DEGRADATION)
    
    def _detect_trend_anomalies(self, strategy_id: str, current_metrics: List[HealthMetric]) -> List[Anomaly]:
        """检测趋势异常"""
        anomalies = []
        
        try:
            history = self.metric_history.get(strategy_id, [])
            if len(history) < 10:  # 需要足够的历史数据
                return anomalies
            
            # 检查每个指标的趋势
            for current_metric in current_metrics:
                historical_values = [m.value for m in history 
                                   if m.name == current_metric.name][-10:]  # 最近10个值
                
                if len(historical_values) >= 5:
                    # 检查是否有持续上升趋势（对于不良指标）
                    if self._is_increasing_trend(historical_values) and current_metric.name in [
                        "cpu_usage", "memory_usage", "error_rate", "response_time"
                    ]:
                        anomaly = Anomaly(
                            type=AnomalyType.PERFORMANCE_DEGRADATION,
                            severity=HealthStatus.WARNING,
                            message=f"{current_metric.name} 呈持续上升趋势",
                            details={
                                "metric_name": current_metric.name,
                                "trend": "increasing",
                                "recent_values": historical_values
                            },
                            strategy_id=strategy_id
                        )
                        anomalies.append(anomaly)
        
        except Exception as e:
            self.logger.error(f"检测趋势异常失败: {e}")
        
        return anomalies
    
    def _is_increasing_trend(self, values: List[float]) -> bool:
        """检查是否为上升趋势"""
        if len(values) < 3:
            return False
        
        increases = 0
        for i in range(1, len(values)):
            if values[i] > values[i-1]:
                increases += 1
        
        return increases >= len(values) * 0.7  # 70%的点都在上升
    
    # 模拟指标获取方法（实际实现需要根据具体策略调整）
    def _get_cpu_usage(self, strategy: BaseStrategy) -> float:
        """获取CPU使用率"""
        # 这里应该实现实际的CPU使用率获取逻辑
        import random
        return random.uniform(10, 50)  # 模拟数据
    
    def _get_memory_usage(self, strategy: BaseStrategy) -> float:
        """获取内存使用率"""
        # 这里应该实现实际的内存使用率获取逻辑
        import random
        return random.uniform(20, 60)  # 模拟数据
    
    def _get_error_rate(self, strategy: BaseStrategy) -> float:
        """获取错误率"""
        # 这里应该实现实际的错误率计算逻辑
        import random
        return random.uniform(0, 3)  # 模拟数据
    
    def _get_response_time(self, strategy: BaseStrategy) -> float:
        """获取响应时间"""
        # 这里应该实现实际的响应时间获取逻辑
        import random
        return random.uniform(100, 800)  # 模拟数据
    
    def _get_order_success_rate(self, strategy: BaseStrategy) -> float:
        """获取订单成功率"""
        # 这里应该实现实际的订单成功率计算逻辑
        import random
        return random.uniform(95, 100)  # 模拟数据
    
    # 默认恢复处理器
    async def _handle_high_error_rate(self, strategy_id: str, anomaly: Anomaly) -> bool:
        """处理高错误率异常"""
        try:
            self.logger.info(f"尝试恢复策略 {strategy_id} 的高错误率问题")
            
            # 实现具体的恢复逻辑
            # 例如：重置连接、清理缓存、重启组件等
            
            await asyncio.sleep(1)  # 模拟恢复过程
            return True
            
        except Exception as e:
            self.logger.error(f"处理高错误率异常失败: {e}")
            return False
    
    async def _handle_stuck_execution(self, strategy_id: str, anomaly: Anomaly) -> bool:
        """处理执行卡住异常"""
        try:
            self.logger.info(f"尝试恢复策略 {strategy_id} 的执行卡住问题")
            
            # 实现具体的恢复逻辑
            # 例如：重启策略、清理队列、重置状态等
            
            await asyncio.sleep(1)  # 模拟恢复过程
            return True
            
        except Exception as e:
            self.logger.error(f"处理执行卡住异常失败: {e}")
            return False
    
    async def _handle_memory_leak(self, strategy_id: str, anomaly: Anomaly) -> bool:
        """处理内存泄漏异常"""
        try:
            self.logger.info(f"尝试恢复策略 {strategy_id} 的内存泄漏问题")
            
            # 实现具体的恢复逻辑
            # 例如：强制垃圾回收、清理缓存、重启策略等
            
            import gc
            gc.collect()  # 强制垃圾回收
            
            await asyncio.sleep(1)  # 模拟恢复过程
            return True
            
        except Exception as e:
            self.logger.error(f"处理内存泄漏异常失败: {e}")
            return False
    
    async def _handle_performance_degradation(self, strategy_id: str, anomaly: Anomaly) -> bool:
        """处理性能下降异常"""
        try:
            self.logger.info(f"尝试恢复策略 {strategy_id} 的性能下降问题")
            
            # 实现具体的恢复逻辑
            # 例如：调整参数、优化配置、减少负载等
            
            await asyncio.sleep(1)  # 模拟恢复过程
            return True
            
        except Exception as e:
            self.logger.error(f"处理性能下降异常失败: {e}")
            return False
    
    async def _handle_resource_exhaustion(self, strategy_id: str, anomaly: Anomaly) -> bool:
        """处理资源耗尽异常"""
        try:
            self.logger.info(f"尝试恢复策略 {strategy_id} 的资源耗尽问题")
            
            # 实现具体的恢复逻辑
            # 例如：释放资源、清理临时文件、重新分配资源等
            
            await asyncio.sleep(1)  # 模拟恢复过程
            return True
            
        except Exception as e:
            self.logger.error(f"处理资源耗尽异常失败: {e}")
            return False